Generative Information Hiding of Iris Feature Data via Gaussian Fuzzy Processing and Advanced Encryption Standard-Based Encryption
Bibliographic record
Abstract
Conventional methods for hiding iris feature data have been hindered by inefficient feature extraction processes and inadequate post-hiding information security, thereby limiting both performance and applicability.To address these challenges, a generative information hiding approach for iris feature data has been developed, based on a Gaussian fuzzy algorithm and Advanced Encryption Standard (AES) encryption.In the preprocessing phase, a weighted averaging technique was employed for greyscale conversion, followed by Gaussian fuzzy smoothing to reduce noise while preserving structural integrity.Subsequent image sharpening was conducted using a Laplacian convolution kernel to enhance edge definition.The iris region was then localized and normalized to a fixed size, ensuring geometric invariance during feature extraction.Two-dimensional Gabor wavelets were utilized for the extraction of robust and discriminative iris features, given their proven effectiveness in capturing both spatial frequency and orientation information.To ensure data confidentiality, the extracted iris features were encrypted using the AES, with a corresponding decryption process integrated within the generative information hiding framework.This dual-layer strategy ensured that both the biometric feature data and the hidden information remained secure against unauthorized access or reconstruction.Experimental validation demonstrated that the proposed method enabled iris feature extraction within ten seconds on standard computing hardware, with a significantly improved data security coefficient compared to conventional techniques.Furthermore, the proposed methodology achieved a high level of imperceptibility and robustness, supporting its application in biometric security systems and privacy-preserving identity verification.These findings suggest that the integration of Gaussian fuzzy processing with secure encryption offers an effective pathway for reliable and efficient iris feature data concealment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".